Image processing based on automatic image-type detection

ABSTRACT

Methods and systems divide an image into image blocks, determine a number of high-variation blocks within valid image blocks, determine a page variance measure by dividing the number of high-variation blocks by the total number of valid image blocks, and classify the image as comprising a halftone and/or text image if the page variance measure exceeds a halftone threshold. Such methods and systems classify the image as being a continuous tone image if the page variance measure does not exceed the halftone threshold and a text index is below a text index threshold, and classify the image as being a continuous tone and text image if the page variance measure does not exceed the halftone threshold and the text index is not below the text index threshold. Further, such methods and systems select an image processing mode based on such image classifications.

BACKGROUND

Systems and methods herein generally relate to image processing and moreparticularly to automated systems for detecting image types.

Copy and scan image processing is generally based on user inputs such asoriginal type, that can be set (for example) to photo or text or mixed(photo and text). Often times there are also sub-types with one originaltype. For example, there could be “printed photo” and “glossy photo”within the original type photo. The user may not always know what toselect, and may end up producing output with compromised image quality.

SUMMARY

Methods and systems herein receive an image into a computerized deviceand divide the image into image blocks, using the computerized device.The image is divided into image blocks by, for example, assigningrectangular geometric regions to the image.

The methods and systems determine a block average by finding the averagepixel value for each of the image blocks, using the computerized device.The pixel value is a darkness measure for each pixel. Such methods andsystems similarly find the squared error for each pixel within each ofthe image blocks (by calculating the squared difference between a pixelvalue and the block average for each pixel in each of the image blocks).The squared error for each pixel is found by multiplying the differencebetween a pixel value and the block average by itself. Such methods andsystems also find the sum of squared errors value for each of the imageblocks by summing, for each of the image blocks, the squared error forall pixels within each of the image blocks, using the computerizeddevice.

Subsequently, these methods and systems examine whether each of theimage blocks comprises a white background block (an invalid block) bydetermining if the block average for each image block is above a whitebackground threshold (and the sum of squared errors value for the imageblock is below an error threshold) using the computerized device. Themethods and systems herein remove the image blocks determined to bewhite background blocks, to leave only valid image blocks of the image,using the computerized device.

The methods and systems herein then calculate the number ofhigh-variation blocks within the valid image blocks by counting thevalid image blocks that have a sum of squared errors value exceeding ablock variation threshold, using the computerized device. Next, suchmethods and systems herein determine a page variance measure by dividingthe number of high-variation blocks by the total number of valid imageblocks, using the computerized device.

Next, these methods and systems classify the image as being a halftone,text, or halftone and text image if the page variance measure exceeds ahalftone threshold; and select a tile-based parallel error diffusionimage processing mode if the image is such a halftone and/or text image.

If the image is not classified as a halftone and/or text image, thesemethods and systems determine a text index by counting the number of thevalid image blocks that have a sum of squared errors value exceeding atext threshold (using the computerized device). Such methods and systemsclassify the image as being a continuous tone image if the page variancemeasure does not exceed the halftone threshold and the text index isbelow a text index threshold. Otherwise, these methods and systemsclassify the image as being a continuous tone and text image if the pagevariance measure does not exceed the halftone threshold and the textindex is not below the text index threshold (again, using thecomputerized device). The methods and systems herein select a sequentialerror diffusion image processing mode for the image if the image isclassified as a continuous tone and text image; and select a clustereddot halftone image processing mode if the image is classified as acontinuous tone image.

The processes of determining the block average, the squared error, thesum of squared errors, and whether each of the image blocks comprises awhite background block, can be performed in parallel processing thatindividually evaluates all of the image blocks simultaneously (at thesame time) to increase processing efficiency. Also, in some examples,these methods and systems can filter all colors except gray from theimage before dividing the image into image blocks, to increaseefficiency.

These and other features are described in, or are apparent from, thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Various exemplary systems and methods are described in detail below,with reference to the attached drawing figures, in which:

FIG. 1 is a flow diagram of various methods herein;

FIG. 2 is a schematic diagram illustrating systems herein;

FIG. 3 is a schematic diagram illustrating devices herein; and

FIG. 4 is a schematic diagram illustrating devices herein.

DETAILED DESCRIPTION

As mentioned above, a user may not always know what image-type to selectfor image processing, and may end up producing output with compromisedimage quality. In view of this, the systems and methods hereinautomatically detect the type of input image, and perform optimalprocessing accordingly, without user intervention. The systems andmethods herein can use a software-based image path with more flexibilityto include different processing options, and the knowledge of theoriginal type can also be used by the systems and methods herein todeliver optimal performance.

Therefore, the systems and methods herein use automatic image-typedetection to control image processing mode selection for image qualityand system performance improvement. In one example, one of threerendering modes is selected based on the automated image-type detectionresults. The systems and methods herein are especially helpful inenabling the transition to lower cost, software-based image pathsolutions.

The systems and methods herein automatically detect the type of scannedinput image and use a block-based approach in the detection process. Thestatistics of the features extracted from the blocks of the image areused to determine if the image is text, halftoned pictorials, continuouspictorials, or some mixture. These systems and methods enable automaticselection of processing modes, reducing the possibility of image qualitycompromises. Further, such systems and methods enable automaticselection of processing methods that provide optimized performance,which is especially useful in software-based image paths.

The following provides a brief overview of processing performed by thesystems and methods herein. More specifically, the systems and methodsherein divide the image into blocks. For example, the blocks can be 4*8pixels each. For each block, these systems and methods calculate theblock average, calculate the square of difference between pixel valueand block average for each pixel in the block (squared error), andcalculate the sum of squared errors (SSE) within block. If a blockbelongs to white background (determined using conditionsSSE<Error_Threshold and Block Average>White_Threshold) it is classifiedas an invalid block.

Further, such systems and methods calculate the page variance measure asbelow:

${{Page}\mspace{14mu} {Variance}\mspace{14mu} {Measure}\mspace{14mu} {{}_{}^{}{}_{}^{}}} = {\frac{{{Number}\mspace{14mu} {of}\mspace{14mu} {valid}\mspace{14mu} {blocks}\mspace{14mu} {with}\mspace{14mu} {SEE}} > {Block\_ Threshold}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {valid}\mspace{14mu} {blocks}}*100}$

The page is classified as halftone/text/halftone and text orcontone/contone and text based on the value of “P.” Specifically, ifP>=P_Thresh, the page is classified as halftone/text/halftone and text.If P<P_Thresh, the page is classified as contone/contone and text. If animage is classified as contone/contone+text, then it is furthervalidated for presence of text. This is done using SSE itself, and noseparate measure used. Specifically, the calculation of the text indexis shown below:

Text Index ‘T’=Number of blocks with SSE>Text_Threshold

If T>T_Thresh, the page is classified as contone+text. The foregoing isexplained in greater detail below.

The detection result can be used to determine various processingoperations. It can be used independently/in the absence of a pixel-levelsegmentation tag, or it can be used alongside the segmentation tag. Forexample, a user-selected mode will determine the tone reproduction curve(TRC) for the whole job; however, with the systems and methods herein,the TRC will be automatically selected based on image-type detectionresults.

Segmentation tag based filtering can be applied, and the mode ororiginal type the user specifies may affect filter selection. Mistakesin mode or image-type selection often result in image quality artifacts.With automatic image-type detection provided by systems and methodsherein, these mistakes can be avoided. Without application of asegmentation tag, often a filter designed for handling different pixeltypes such as text and halftone is applied on the whole image. However,the automatic image-type detection of the systems and methods herein canuse bigger, more sophisticated filters just on the types of images thatneed it, and use smaller filters for the rest of the image-types. Thisis especially useful for software-based image paths in which the filtersare not fixed, and smaller filters provide real saving in computationtime.

Another cause of image quality degradation is the application of thewrong rendering method, resulting from the wrong mode or image-typebeing selected by the user. However, the automatic image-type detectionresult provided by systems and methods herein can be used to determinethe suitable rendering method, thus increasing the robustness of theimage path. In a software-based image path, controlling the renderingbased on automatic image-type detection also opens up opportunities forperformance optimization.

One performance bottleneck in software-based image processing is errordiffusion. The nature of the error diffusion operation hinders parallelprocessing otherwise made possible with multiple cores available inmodern computing devices. However, the systems and methods hereinsatisfy both image quality and system performance requirements of realproducts, and provide rendering method selection based on image-typedetection. The systems and methods described herein provide many modesof operation for rendering in the system.

One mode is tile-based parallel processing for error diffusion, with acertain amount of overlapping between the tiles. A second mode isregular halftone screening, and a third mode is regular sequential errordiffusion. Tile-based processing is used for halftoned originals, orfull text original, or originals with a mixture of halftones and text.This is based on the observation that due to the busy structure presentin the original halftone and text, there are not many boundary artifactsnoticeable with tile-based parallel error diffusion, when some amount oftile overlapping is used.

Continuous tone originals, on the other hand, are difficult to renderwith parallel error diffusion without showing boundary artifacts, butare actually more preferable for rendering with regular halftoneprocessing. For images mixed with continuous tone content and othercontents such as halftone and text, which is not common, regularsequential error diffusion is applied.

If the image does not contain continuous tone pictorial content, thensystems and methods herein select tile-based parallel error diffusion asthe rendering method. Or else, if the image does not contain textcontent, then systems and methods herein select clustered dot halftoneas the rendering method. Otherwise, systems and methods herein selectregular sequential error diffusion as the rendering method.

With the proposed method, the processing of most originals can takeadvantage of the performance enabled by a multi-core device, leavingonly the rarely encountered original types for conventional sequentialerror diffusion.

FIG. 1 is flowchart illustrating exemplary methods herein. In item 100,these methods and systems receive an image into a computerized deviceand, in item 102, divide the image into image blocks, using thecomputerized device. The image is divided into image blocks, forexample, by assigning rectangular geometric regions to the image, whereall blocks are the same size to balance processing.

In item 104, the methods and systems determine a block average byfinding the average pixel value for each of the image blocks, using thecomputerized device. The pixel value is a darkness measure for each thepixel, such as an “on” or “off” indicator for marking material such asinks or toners. Such methods and systems similarly find the squarederror for each pixel within each of the image blocks (by calculating thesquared difference between a pixel value and the block average for eachpixel in each of the image blocks) in item 104. The squared error foreach pixel is found by multiplying the difference between a pixel valueand the block average by itself. Such methods and systems also find thesum of squared errors value for each of the image blocks in item 104 bysumming, for each of the image blocks, the squared error for all pixelswithin each of the image blocks, using the computerized device. Thisprocessing can be performed in parallel processing that individuallyevaluates all of the image blocks simultaneously (at the same time) toincrease processing efficiency

Subsequently, these methods and systems examine whether each of theimage blocks comprises a white background block (an invalid block) initem 106 by determining if the block average for each image block isabove a white background threshold (and the sum of squared errors valuefor the image block is below an error threshold) using the computerizeddevice. The methods and systems herein remove the image blocksdetermined to be white background blocks as invalid blocks 108, to leaveonly valid image blocks of the image 110, using the computerized device.

The methods and systems herein then calculate the number ofhigh-variation blocks within the valid image blocks in item 112 bycounting the valid image blocks that have a sum of squared errors valueexceeding a block variation threshold, using the computerized device.Next, such methods and systems herein determine a page variance measurein item 114 by dividing the number of high-variation blocks by the totalnumber of valid image blocks and multiplying the result by 100, usingthe computerized device.

Next, in item 120, these methods and systems classify the image as beinga halftone, text, or halftone and text image 122 if the page variancemeasure exceeds a halftone threshold; and, in item 124 select atile-based parallel error diffusion image processing mode if the imageis such a halftone and/or text image.

If the image is not classified as a halftone and/or text image in item120, these methods and systems determine a text index in item 126 bycounting the number of the valid image blocks that have a sum of squarederrors value exceeding a text threshold (using the computerized device).

In item 130, such methods and systems classify the image as being acontinuous tone image 132 if the page variance measure does not exceedthe halftone threshold and the text index is below a text indexthreshold. Otherwise, these methods and systems classify the image initem 130 as being a continuous tone and text image 140 if the pagevariance measure does not exceed the halftone threshold and the textindex is not below the text index threshold (again, using thecomputerized device). The methods and systems herein select a clustereddot halftone image processing mode 134, if the image is classified as acontinuous tone image 132; and select a sequential error diffusion imageprocessing mode for the image 142 if the image is classified as acontinuous tone and text image 140.

The determination of the block average, the squared error, the sum ofsquared errors (104), and whether each of the image blocks comprises awhite background block (106), can be performed in parallel processingthat individually evaluates all of the image blocks simultaneously (atthe same time) to increase processing efficiency. Also, in item 102,these methods and systems can filter all colors except gray from theimage before dividing the image into image blocks, to increaseefficiency.

As shown in FIG. 2, exemplary system systems and methods herein includevarious computerized devices 200, 204 located at various differentphysical locations 206. The computerized devices 200, 204 can includeprint servers, printing devices, personal computers, etc., and are incommunication (operatively connected to one another) by way of a localor wide area (wired or wireless) network 202.

FIG. 3 illustrates a computerized device 200, which can be used withsystems and methods herein and can comprise, for example, a printserver, a personal computer, a portable computing device, etc. Thecomputerized device 200 includes a controller/processor 224 and acommunications port (input/output) 226 operatively connected to theprocessor 224 and to the computerized network 202 external to thecomputerized device 200. Also, the computerized device 200 can includeat least one accessory functional component, such as a graphic userinterface assembly 236 that also operates on the power supplied from theexternal power source 228 (through the power supply 222).

The input/output device 226 is used for communications to and from thecomputerized device 200. The processor 224 controls the various actionsof the computerized device. A non-transitory computer storage mediumdevice 220 (which can be optical, magnetic, capacitor based, etc.) isreadable by the processor 224 and stores instructions that the processor224 executes to allow the computerized device to perform its variousfunctions, such as those described herein. Thus, as shown in FIG. 3, abody housing 200 has one or more functional components that operate onpower supplied from an alternating current (AC) source 228 by the powersupply 222. The power supply 222 can comprise a power storage element(e.g., a battery, etc).

FIG. 4 illustrates a computerized device that is a printing device 204,which can be used with systems and methods herein and can comprise, forexample, a printer, copier, multi-function machine, multi-functiondevice (MFD), etc. The printing device 204 includes many of thecomponents mentioned above and at least one marking device (printingengines) 210 operatively connected to the processor 224, a media path216 positioned to supply sheets of media from a sheet supply 214 to themarking device(s) 210, etc. After receiving various markings from theprinting engine(s), the sheets of media can optionally pass to afinisher 208 which can fold, staple, sort, etc., the various printedsheets. Also, the printing device 204 can include at least one accessoryfunctional component (such as a scanner/document handler 212, etc.) thatalso operates on the power supplied from the external power source 228(through the power supply 222).

Many computerized devices are discussed above. Computerized devices thatinclude chip-based central processing units (CPU's), input/outputdevices (including graphic user interfaces (GUI), memories, comparators,processors, etc. are well-known and readily available devices producedby manufacturers such as Dell Computers, Round Rock Tex., USA and AppleComputer Co., Cupertino Calif., USA. Such computerized devices commonlyinclude input/output devices, power supplies, processors, electronicstorage memories, wiring, etc., the details of which are omittedherefrom to allow the reader to focus on the salient aspects of thesystems and methods described herein. Similarly, scanners and othersimilar peripheral equipment are available from Xerox Corporation,Norwalk, Conn., USA and the details of such devices are not discussedherein for purposes of brevity and reader focus.

The terms printer or printing device as used herein encompasses anyapparatus, such as a digital copier, bookmaking machine, facsimilemachine, multi-function machine, etc., which performs a print outputtingfunction for any purpose. The details of printers, printing engines,etc., are well-known and are not described in detail herein to keep thisdisclosure focused on the salient features presented. The systems andmethods herein can encompass systems and methods that print in color,monochrome, or handle color or monochrome image data. All foregoingsystems and methods are specifically applicable to electrostatographicand/or xerographic machines and/or processes.

A “pixel” refers to the smallest segment into which an image can bedivided. Received pixels of an input image are associated with a colorvalue defined in terms of a color space, such as color, intensity,lightness, brightness, or some mathematical transformation thereof.Pixel color values may be converted to a chrominance-luminance spaceusing, for instance, a RBG-to-YCbCr converter to obtain luminance (Y)and chrominance (Cb,Cr) values. It should be appreciated that pixels maybe represented by values other than RGB or YCbCr.

Thus, an image input device is any device capable of obtaining colorpixel values from a color image. The set of image input devices isintended to encompass a wide variety of devices such as, for example,digital document devices, computer systems, memory and storage devices,networked platforms such as servers and client devices which can obtainpixel values from a source device, and image capture devices. The set ofimage capture devices includes scanners, cameras, photography equipment,facsimile machines, photo reproduction equipment, digital printingpresses, xerographic devices, and the like. A scanner is one imagecapture device that optically scans images, print media, and the like,and converts the scanned image into a digitized format. Common scanningdevices include variations of the flatbed scanner, generally known inthe arts, wherein specialized image receptors move beneath a platen andscan the media placed on the platen. Modern digital scanners typicallyincorporate a charge-coupled device (CCD) or a contact image sensor(CIS) as the image sensing receptor(s). The scanning device produces asignal of the scanned image data. Such a digital signal containsinformation about pixels such as color value, intensity, and theirlocation within the scanned image.

Further, an image output device is any device capable of rendering theimage. The set of image output devices includes digital documentreproduction equipment and other copier systems as are widely known incommerce, photographic production and reproduction equipment, monitorsand other displays, computer workstations and servers, including a widevariety of color marking devices, and the like.

To render an image is to reduce the image data (or a signal thereof) toviewable form; store the image data to memory or a storage device forsubsequent retrieval; or communicate the image data to another device.Such communication may take the form of transmitting a digital signal ofthe image data over a network.

A contone is a characteristic of a color image such that the image hasall the values (0 to 100%) of gray (black/white) or color in it. Acontone can be approximated by millions of gradations of black/white orcolor values. The granularity of computer screens (i.e., pixel size) canlimit the ability to display absolute contones. The term halftoningmeans a process of representing a contone image by a bi-level image suchthat, when viewed from a suitable distance, the bi-level image gives thesame impression as the contone image. Halftoning reduces the number ofquantization levels per pixel in a digital image. Over the long historyof halftoning, a number of halftoning techniques have been developedwhich are adapted for different applications.

Traditional clustered dot halftones were restricted to a singlefrequency because they were generated using periodic gratings that couldnot be readily varied spatially. Halftoning techniques are widelyemployed in the printing and display of digital images and are usedbecause the physical processes involved are binary in nature or becausethe processes being used have been restricted to binary operation forreasons of cost, speed, memory, or stability in the presence of processfluctuations. Classical halftone screening applies a mask of thresholdvalues to each color of the multi-bit image. Thresholds are stored as amatrix in a repetitive pattern. Each tile of the repetitive pattern ofthe matrix is a halftone cell. Digital halftones generated usingthreshold arrays that tile the image plane were originally designed tobe periodic for simplicity and to minimize memory requirements. With theincrease in computational power and memory, these constraints becomeless stringent. Digital halftoning uses a raster image or bitmap withinwhich each monochrome picture element or pixel may be ON or OFF (ink orno ink). Consequently, to emulate the photographic halftone cell, thedigital halftone cell must contain groups of monochrome pixels withinthe same-sized cell area. In addition, the terms automated orautomatically mean that once a process is started (by a machine or auser), one or more machines perform the process without further inputfrom any user.

It will be appreciated that the above-disclosed and other features andfunctions, or alternatives thereof, may be desirably combined into manyother different systems or applications. Various presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may be subsequently made by those skilled in the art which arealso intended to be encompassed by the following claims. Unlessspecifically defined in a specific claim itself, steps or components ofthe systems and methods herein cannot be implied or imported from anyabove example as limitations to any particular order, number, position,size, shape, angle, color, or material.

1. A method comprising: receiving an image into a computerized device;automatically determining an image-type of said image; and automaticallyselecting an image processing mode based on said image-type of saidimage.
 2. The method according to claim 1, said automaticallydetermining said image-type comprising: automatically dividing saidimage into image blocks, using said computerized device; automaticallydetermining a block average by determining an average pixel value foreach of said image blocks, using said computerized device; automaticallydetermining a squared error for each pixel within each of said imageblocks by calculating a squared difference between a pixel value andsaid block average for each said pixel in each of said image blocks,using said computerized device; automatically determining a sum ofsquared errors value for each of said image blocks, using saidcomputerized device; automatically removing ones of said image blocksdetermined to be a white background block to produce valid image blocksof said image, using said computerized device; automatically determininga number of high-variation blocks within said valid image blocks bycounting ones of said valid image blocks that have said sum of squarederrors value exceeding a block variation threshold, using saidcomputerized device; automatically determining a page variance measureby dividing said number of high-variation blocks by a total number ofsaid valid image blocks, using said computerized device; automaticallyclassifying said image as comprising a halftone and text image based onsaid page variance measure exceeding a halftone threshold, using saidcomputerized device; automatically determining a text index by countinga number of said valid image blocks that have said sum of squared errorsvalue exceeding a text threshold, using said computerized device;automatically classifying said image as comprising a continuous toneimage based on said page variance measure not exceeding said halftonethreshold and said text index being below a text index threshold, usingsaid computerized device; and automatically classifying said image ascomprising a continuous tone and text image based on said page variancemeasure not exceeding said halftone threshold and said text index notbeing below said text index threshold, using said computerized device,said automatically selecting said image processing mode comprisingautomatically selecting said image processing mode based on said imagebeing one of said halftone and text image, said continuous tone and textimage, and said continuous tone image.
 3. The method according to claim2, said determining said squared error for each pixel comprisingmultiplying said difference between a pixel value and said block averageby itself.
 4. The method according to claim 2, said dividing said imageinto image blocks comprising assigning rectangular geometric regions tosaid image.
 5. The method according to claim 2, further comprisingfiltering all colors except gray from said image before performing saiddividing said image into image blocks.
 6. The method according to claim2, said pixel value comprising a darkness measure for each said pixel.7. A method comprising: receiving an image into a computerized device;automatically dividing said image into image blocks, using saidcomputerized device; automatically determining a block average bydetermining an average pixel value for each of said image blocks, usingsaid computerized device; automatically determining a squared error foreach pixel within each of said image blocks by calculating a squareddifference between a pixel value and said block average for each saidpixel in each of said image blocks, using said computerized device;automatically determining a sum of squared errors value for each of saidimage blocks by summing, for each of said image blocks, said squarederror for all pixels within each of said image blocks, using saidcomputerized device; automatically determining whether each of saidimage blocks comprises a white background block of said image bydetermining if said block average for each of said image blocks exceedsa white background threshold, using said computerized device;automatically removing ones of said image blocks determined to be saidwhite background block to produce valid image blocks of said image,using said computerized device; automatically determining a number ofhigh-variation blocks within said valid image blocks by counting ones ofsaid valid image blocks that have said sum of squared errors valueexceeding a block variation threshold, using said computerized device;automatically determining a page variance measure by dividing saidnumber of high-variation blocks by a total number of said valid imageblocks, using said computerized device; automatically classifying saidimage as comprising a halftone and text image based on said pagevariance measure exceeding a halftone threshold, using said computerizeddevice; automatically determining a text index by counting a number ofsaid valid image blocks that have said sum of squared errors valueexceeding a text threshold, using said computerized device;automatically classifying said image as comprising a continuous toneimage based on said page variance measure not exceeding said halftonethreshold and said text index being below a text index threshold, usingsaid computerized device; automatically classifying said image ascomprising a continuous tone and text image based on said page variancemeasure not exceeding said halftone threshold and said text index notbeing below said text index threshold, using said computerized device;and automatically selecting an image processing mode based on said imagebeing one of said halftone and text image, said continuous tone and textimage, and said continuous tone image.
 8. The method according to claim7, said determining said block average, said determining said squarederror, said determining said sum of squared errors, and said determiningwhether each of said image blocks comprises a white background block,being performed in parallel processing that individually evaluates allof said image blocks simultaneously.
 9. The method according to claim 7,said determining said squared error for each pixel comprisingmultiplying said difference between a pixel value and said block averageby itself.
 10. The method according to claim 7, said dividing said imageinto image blocks comprising assigning rectangular geometric regions tosaid image.
 11. The method according to claim 7, further comprisingfiltering all colors except gray from said image before performing saiddividing said image into image blocks.
 12. The method according to claim7, said pixel value comprising a darkness measure for each said pixel.13. A method comprising: receiving an image into a computerized device;automatically dividing said image into image blocks, using saidcomputerized device; automatically determining a block average bydetermining an average pixel value for each of said image blocks, usingsaid computerized device; automatically determining a squared error foreach pixel within each of said image blocks by calculating a squareddifference between a pixel value and said block average for each saidpixel in each of said image blocks, using said computerized device;automatically determining a sum of squared errors value for each of saidimage blocks by summing, for each of said image blocks, said squarederror for all pixels within each of said image blocks, using saidcomputerized device; automatically determining whether each of saidimage blocks comprises a white background block of said image bydetermining if said block average for each of said image blocks exceedsa white background threshold, using said computerized device;automatically removing ones of said image blocks determined to be saidwhite background block to produce valid image blocks of said image,using said computerized device; automatically determining a number ofhigh-variation blocks within said valid image blocks by counting ones ofsaid valid image blocks that have said sum of squared errors valueexceeding a block variation threshold, using said computerized device;automatically determining a page variance measure by dividing saidnumber of high-variation blocks by a total number of said valid imageblocks, using said computerized device; automatically classifying saidimage as comprising a halftone and text image based on said pagevariance measure exceeding a halftone threshold, using said computerizeddevice; automatically determining a text index by counting a number ofsaid valid image blocks that have said sum of squared errors valueexceeding a text threshold, using said computerized device;automatically classifying said image as comprising a continuous toneimage based on said page variance measure not exceeding said halftonethreshold and said text index being below a text index threshold, usingsaid computerized device; automatically classifying said image ascomprising a continuous tone and text image based on said page variancemeasure not exceeding said halftone threshold and said text index notbeing below said text index threshold, using said computerized device;automatically selecting a tile-based parallel error diffusion imageprocessing mode based on said image being said halftone and text image;automatically selecting a sequential error diffusion image processingmode based on said image being said continuous tone and text image; andautomatically selecting a clustered dot halftone image processing modebased on said image being said continuous tone image.
 14. The methodaccording to claim 13, said determining said block average, saiddetermining said squared error, said determining said sum of squarederrors, and said determining whether each of said image blocks comprisesa white background block, being performed in parallel processing thatindividually evaluates all of said image blocks simultaneously.
 15. Themethod according to claim 13, said determining said squared error foreach pixel comprising multiplying said difference between a pixel valueand said block average by itself.
 16. The method according to claim 13,said dividing said image into image blocks comprising assigningrectangular geometric regions to said image.
 17. The method according toclaim 13, further comprising filtering all colors except gray from saidimage before performing said dividing said image into image blocks. 18.The method according to claim 13, said pixel value comprising a darknessmeasure for each said pixel.
 19. An image processing device comprising:an input and output device receiving an image; and a processoroperatively connected to said input and output device, said processor:automatically dividing said image into image blocks, automaticallydetermining a block average by determining an average pixel value foreach of said image blocks, automatically determining a squared error foreach pixel within each of said image blocks by calculating a squareddifference between a pixel value and said block average for each saidpixel in each of said image blocks, automatically determining a sum ofsquared errors value for each of said image blocks, automaticallyremoving ones of said image blocks determined to a white backgroundblock to produce valid image blocks of said image, automaticallydetermining a number of high-variation blocks within said valid imageblocks by counting ones of said valid image blocks that have said sum ofsquared errors value exceeding a block variation threshold,automatically determining a page variance measure by dividing saidnumber of high-variation blocks by a total number of said valid imageblocks, automatically classifying said image as comprising a halftoneand text image based on said page variance measure exceeding a halftonethreshold, automatically determining a text index by counting a numberof said valid image blocks that have said sum of squared errors valueexceeding a text threshold, automatically classifying said image ascomprising a continuous tone image based on said page variance measurenot exceeding said halftone threshold and said text index being below atext index threshold, automatically classifying said image as comprisinga continuous tone and text image based on said page variance measure notexceeding said halftone threshold and said text index not being belowsaid text index threshold, and automatically selecting an imageprocessing mode based on said image being one of said halftone and textimage, said continuous tone and text image, and said continuous toneimage.
 20. The image processing device according to claim 19, saiddetermining said block average, said determining said squared error,said determining said sum of squared errors, and said determiningwhether each of said image blocks comprises a white background block,being performed in parallel processing that individually evaluates allof said image blocks simultaneously.
 21. The image processing deviceaccording to claim 19, said determining said squared error for eachpixel comprising multiplying said difference between a pixel value andsaid block average by itself.
 22. The image processing device accordingto claim 19, said dividing said image into image blocks comprisingassigning rectangular geometric regions to said image.
 23. The imageprocessing device according to claim 19, said processor filtering allcolors except gray from said image before performing said dividing saidimage into image blocks.
 24. The method according to claim 1, said pixelvalue comprising a darkness measure for each said pixel.